Here in this example we have taken resume samples to identify which are simliar documents are their respective similarity ratio.
import numpy as np
import pandas as pd
import nltk
import re
import os
import codecs
from sklearn import feature_extraction
import mpld3
import os
listdir = []
path = 'D:/xray production/resume backup/Docs_Backup/Resume_set_01/train'
listdir = [os.path.join(dirpath, f)
for dirpath, dirnames, files in os.walk(path)
for f in files if f.endswith('.txt')]
# here I define a tokenizer and stemmer which returns the set of stems in the text that it is passed
def tokenize_and_stem(text):
# first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token
tokens = [word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)]
filtered_tokens = []
# filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation)
for token in tokens:
if re.search('[a-zA-Z]', token):
filtered_tokens.append(token)
stems = [stemmer.stem(t) for t in filtered_tokens]
return stems
def tokenize_only(text):
# first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token
tokens = [word.lower() for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)]
filtered_tokens = []
# filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation)
for token in tokens:
if re.search('[a-zA-Z]', token):
filtered_tokens.append(token)
return filtered_tokens
import logging
import os
import glob2
from nltk.corpus import stopwords
from nltk.tokenize import wordpunct_tokenize
import nltk
import re
import gensim
stop_words = set(stopwords.words('english'))
stop_words.update(['.', ',', '"', "'", '?', '!', ':', ';', '(', ')',
'[', ']', '{', '}','%','&','+','$','@','~','-','_','://','--','[]']) # remove it if you need punctuation
from nltk.tokenize import RegexpTokenizer
from datetime import datetime
# load nltk's SnowballStemmer as variabled 'stemmer'
from nltk.stem.snowball import SnowballStemmer
stemmer = SnowballStemmer("english")
#not super pythonic, no, not at all.
#use extend so it's a big flat list of vocab
synopses = []
totalvocab_stemmed = []
totalvocab_tokenized = []
for fle in listdir:
# open the file and then call .read() to get the text
with open(fle, encoding="latin-1") as f:
text = f.read()
s = text
synopses.append(s)
for i in synopses:
allwords_stemmed = tokenize_and_stem(i) #for each item in 'synopses', tokenize/stem
totalvocab_stemmed.extend(allwords_stemmed) #extend the 'totalvocab_stemmed' list
allwords_tokenized = tokenize_only(i)
totalvocab_tokenized.extend(allwords_tokenized)
vocab_frame = pd.DataFrame({'words': totalvocab_tokenized}, index = totalvocab_stemmed)
print('there are ' + str(vocab_frame.shape[0]) + ' items in vocab_frame')
there are 248824 items in vocab_frame
print(vocab_frame.head())
words applic applicant categori category profession professional requisit requisition code code
from sklearn.feature_extraction.text import TfidfVectorizer
#define vectorizer parameters
tfidf_vectorizer = TfidfVectorizer(max_df=100000, max_features=700000,
min_df=0, stop_words='english',
use_idf=True, tokenizer=tokenize_and_stem, ngram_range=(1,3))
%time tfidf_matrix = tfidf_vectorizer.fit_transform(synopses) #fit the vectorizer to synopses
print(tfidf_matrix.shape)
Wall time: 9.56 s (318, 259405)
terms = tfidf_vectorizer.get_feature_names()
from sklearn.metrics.pairwise import cosine_similarity
dist = 1 - cosine_similarity(tfidf_matrix)
from sklearn.cluster import KMeans
num_clusters = 5
km = KMeans(n_clusters=num_clusters)
%time km.fit(tfidf_matrix)
clusters = km.labels_.tolist()
Wall time: 15.3 s
from sklearn.externals import joblib
#uncomment the below to save your model
#since I've already run my model I am loading from the pickle
joblib.dump(km, 'doc_cluster.pkl')
km = joblib.load('doc_cluster.pkl')
clusters = km.labels_.tolist()
films = { 'title': listdir, 'synopsis': synopses, 'cluster': clusters }
frame = pd.DataFrame(films, index = [clusters] , columns = ['title', 'cluster', 'synopsis'])
frame['cluster'].value_counts()
3 79 2 66 4 61 1 57 0 55 Name: cluster, dtype: int64
from __future__ import print_function
print("Top terms per cluster:")
print()
#sort cluster centers by proximity to centroid
order_centroids = km.cluster_centers_.argsort()[:, ::-1]
for i in range(num_clusters):
print("Cluster %d words:" % i, end='')
for ind in order_centroids[i, :6]: #replace 6 with n words per cluster
print(' %s' % vocab_frame.ix[terms[ind].split(' ')].values.tolist()[0][0].encode('utf-8', 'ignore'), end=',')
print() #add whitespace
print() #add whitespace
print("Cluster %d titles:" % i, end='')
for title in frame.ix[i]['title'].values.tolist():
print(' %s,' % title, end='')
print() #add whitespace
print() #add whitespace
print()
print()
Top terms per cluster: Cluster 0 words: b'test', b'project', b'development', b'reports', b'oracle', b'used', Cluster 0 titles: D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192752.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192820.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192822.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192830.txt,
C:\Users\amishra\AppData\Local\Continuum\Anaconda3\lib\site-packages\ipykernel_launcher.py:12: DeprecationWarning: .ix is deprecated. Please use .loc for label based indexing or .iloc for positional indexing See the documentation here: http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix if sys.path[0] == '': C:\Users\amishra\AppData\Local\Continuum\Anaconda3\lib\site-packages\ipykernel_launcher.py:17: DeprecationWarning: .ix is deprecated. Please use .loc for label based indexing or .iloc for positional indexing See the documentation here: http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix
D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192835.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192837.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192842.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192846.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192848.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192850.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192851.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192852.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192854.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192855.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192886.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192888.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192892.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192893.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192906.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192907.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192912.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192916.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192918.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192929.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192937.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192939.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192940.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192941.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192944.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192945.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192954.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192959.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192975.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192976.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192978.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192980.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192983.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192988.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192993.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192997.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192998.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193003.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193008.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193014.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193018.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193024.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193025.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193027.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193045.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193054.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193057.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193058.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193063.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193066.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193073.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193074.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193077.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193078.txt, Cluster 1 words: b'military', b'army', b'recruitmilitary', b'school', b'working', b'document', Cluster 1 titles: D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192765.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192766.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192767.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192768.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192769.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192770.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192771.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192772.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192773.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192774.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192775.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192776.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192777.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192778.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192779.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192780.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192781.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192782.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192783.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192784.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192785.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192786.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192787.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192788.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192789.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192790.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192791.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192792.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192793.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192794.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192795.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192796.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192797.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192798.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192799.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192800.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192801.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192802.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192803.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192804.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192805.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192806.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192807.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192808.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192809.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192810.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192811.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192812.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192813.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192814.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192815.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192817.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192818.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192819.txt, Cluster 2 words: b'network', b'servers', b'configuration', b'installation', b'hardware', b'manager', Cluster 2 titles: D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192751.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192753.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192816.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192825.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192832.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192833.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192839.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192841.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192844.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192853.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192856.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192859.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192860.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192862.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192864.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192867.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192868.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192869.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192872.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192874.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192875.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192877.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192881.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192885.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192890.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192891.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192895.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192898.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192899.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192902.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192903.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192910.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192913.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192919.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192930.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192943.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192977.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193006.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193034.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193035.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193047.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193048.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193050.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193056.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193059.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193069.txt, Cluster 3 words: b'manager', b'sales', b'marketing', b'working', b'hr', b'productivity', Cluster 3 titles: D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192821.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192823.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192824.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192826.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192828.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192829.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192831.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192834.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192836.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192838.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192840.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192843.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192845.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192847.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192849.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192857.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192858.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192861.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192863.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192866.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192870.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192873.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192876.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192878.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192879.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192880.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192882.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192883.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192884.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192887.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192889.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192894.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192896.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192900.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192901.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192904.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192905.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192908.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192909.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192911.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192920.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192921.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192924.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192928.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192936.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192950.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192952.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192967.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192973.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192990.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192999.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193000.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193007.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193013.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193015.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193016.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193020.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193022.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193028.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193029.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193030.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193031.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193032.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193033.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193036.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193038.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193039.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193042.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193043.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193044.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193046.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193049.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193051.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193052.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193053.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193060.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193061.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193062.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193064.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193065.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193067.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193068.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193070.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193071.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193072.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193075.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193079.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193080.txt, Cluster 4 words: b'specified', b'c', b'resumes', b'computer', b'skills', b'date', Cluster 4 titles: D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192827.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192865.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192871.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192897.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192914.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192915.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192917.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192922.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192923.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192925.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192926.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192927.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192931.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192932.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192933.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192934.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192935.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192938.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192942.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192946.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192947.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192948.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192949.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192951.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192953.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192956.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192957.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192958.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192960.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192961.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192962.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192963.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192964.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192965.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192966.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192968.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192969.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192970.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192971.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192972.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192974.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192979.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192981.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192982.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192984.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192985.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192986.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192987.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192989.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192991.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192992.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192994.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192995.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192996.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193001.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193002.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193004.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193005.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193009.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193010.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193011.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193012.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193017.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193019.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193021.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193023.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193026.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193037.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193040.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193041.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193055.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193076.txt,
import os # for os.path.basename
import matplotlib.pyplot as plt
import matplotlib as mpl
from sklearn.manifold import MDS
MDS()
# convert two components as we're plotting points in a two-dimensional plane
# "precomputed" because we provide a distance matrix
# we will also specify `random_state` so the plot is reproducible.
mds = MDS(n_components=2, dissimilarity="precomputed", random_state=1)
pos = mds.fit_transform(dist) # shape (n_components, n_samples)
xs, ys = pos[:, 0], pos[:, 1]
print()
print()
cluster_colors = {0: '#1b9e77', 1: '#d95f02', 2: '#7570b3', 3: '#e7298a', 4: '#66a61e'}
#set up cluster names using a dict
cluster_names = {0: 'first cluster',
1: 'second cluster',
2: 'third cluster',
3: 'fourth cluster',
4: 'fifth cluster'}
#some ipython magic to show the matplotlib plots inline
%matplotlib inline
#create data frame that has the result of the MDS plus the cluster numbers and titles
df = pd.DataFrame(dict(x=xs, y=ys, label=clusters, title=listdir))
#group by cluster
groups = df.groupby('label')
# set up plot
fig, ax = plt.subplots(figsize=(17, 9)) # set size
ax.margins(0.05) # Optional, just adds 5% padding to the autoscaling
#iterate through groups to layer the plot
#note that I use the cluster_name and cluster_color dicts with the 'name' lookup to return the appropriate color/label
for name, group in groups:
ax.plot(group.x, group.y, marker='o', linestyle='', ms=12,
label=cluster_names[name], color=cluster_colors[name],
mec='none')
ax.set_aspect('auto')
ax.tick_params(\
axis= 'x', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
labelbottom='off')
ax.tick_params(\
axis= 'y', # changes apply to the y-axis
which='both', # both major and minor ticks are affected
left='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
labelleft='off')
ax.legend(numpoints=1) #show legend with only 1 point
#add label in x,y position with the label as the film title
for i in range(len(df)):
ax.text(df.ix[i]['x'], df.ix[i]['y'], df.ix[i]['title'], size=8)
plt.show() #show the plot
#uncomment the below to save the plot if need be
plt.savefig('clusters_small_noaxes.png', dpi=200)
C:\Users\amishra\AppData\Local\Continuum\Anaconda3\lib\site-packages\ipykernel_launcher.py:37: DeprecationWarning: .ix is deprecated. Please use .loc for label based indexing or .iloc for positional indexing See the documentation here: http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix
plt.close()
#define custom toolbar location
class TopToolbar(mpld3.plugins.PluginBase):
"""Plugin for moving toolbar to top of figure"""
JAVASCRIPT = """
mpld3.register_plugin("toptoolbar", TopToolbar);
TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);
TopToolbar.prototype.constructor = TopToolbar;
function TopToolbar(fig, props){
mpld3.Plugin.call(this, fig, props);
};
TopToolbar.prototype.draw = function(){
// the toolbar svg doesn't exist
// yet, so first draw it
this.fig.toolbar.draw();
// then change the y position to be
// at the top of the figure
this.fig.toolbar.toolbar.attr("x", 150);
this.fig.toolbar.toolbar.attr("y", 400);
// then remove the draw function,
// so that it is not called again
this.fig.toolbar.draw = function() {}
}
"""
def __init__(self):
self.dict_ = {"type": "toptoolbar"}
#create data frame that has the result of the MDS plus the cluster numbers and titles
df = pd.DataFrame(dict(x=xs, y=ys, label=clusters, title=listdir))
#group by cluster
groups = df.groupby('label')
#define custom css to format the font and to remove the axis labeling
css = """
text.mpld3-text, div.mpld3-tooltip {
font-family:Arial, Helvetica, sans-serif;
}
g.mpld3-xaxis, g.mpld3-yaxis {
display: none; }
svg.mpld3-figure {
margin-left: -200px;}
"""
# Plot
fig, ax = plt.subplots(figsize=(14,6)) #set plot size
ax.margins(0.03) # Optional, just adds 5% padding to the autoscaling
#iterate through groups to layer the plot
#note that I use the cluster_name and cluster_color dicts with the 'name' lookup to return the appropriate color/label
for name, group in groups:
points = ax.plot(group.x, group.y, marker='o', linestyle='', ms=18,
label=cluster_names[name], mec='none',
color=cluster_colors[name])
ax.set_aspect('auto')
labels = [i for i in group.title]
#set tooltip using points, labels and the already defined 'css'
tooltip = mpld3.plugins.PointHTMLTooltip(points[0], labels,
voffset=10, hoffset=10, css=css)
#connect tooltip to fig
mpld3.plugins.connect(fig, tooltip, TopToolbar())
#set tick marks as blank
ax.axes.get_xaxis().set_ticks([])
ax.axes.get_yaxis().set_ticks([])
#set axis as blank
ax.axes.get_xaxis().set_visible(False)
ax.axes.get_yaxis().set_visible(False)
ax.legend(numpoints=1) #show legend with only one dot
mpld3.display() #show the plot
#uncomment the below to export to html
html = mpld3.fig_to_html(fig)
from scipy.cluster.hierarchy import ward, dendrogram
linkage_matrix = ward(dist) #define the linkage_matrix using ward clustering pre-computed distances
fig, ax = plt.subplots(figsize=(15, 20)) # set size
ax = dendrogram(linkage_matrix, orientation="right", labels=listdir);
plt.tick_params(\
axis= 'x', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
labelbottom='off')
plt.tight_layout() #show plot with tight layout
#uncomment below to save figure
plt.savefig('ward_clusters.png', dpi=200) #save figure as ward_clusters
plt.close()
#strip any proper names from a text...unfortunately right now this is yanking the first word from a sentence too.
import string
def strip_proppers(text):
# first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token
tokens = [word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent) if word.islower()]
return "".join([" "+i if not i.startswith("'") and i not in string.punctuation else i for i in tokens]).strip()
#strip any proper nouns (NNP) or plural proper nouns (NNPS) from a text
from nltk.tag import pos_tag
def strip_proppers_POS(text):
tagged = pos_tag(text.split()) #use NLTK's part of speech tagger
non_propernouns = [word for word,pos in tagged if pos != 'NNP' and pos != 'NNPS']
return non_propernouns
from gensim import corpora, models, similarities
#remove proper names
%time preprocess = [strip_proppers(doc) for doc in synopses]
#tokenize
%time tokenized_text = [tokenize_and_stem(text) for text in preprocess]
Wall time: 3.58 s Wall time: 2.72 s
stop_words = set(stopwords.words('english'))
stop_words.update(['.', ',', '"', "'", '?', '!', ':', ';', '(', ')',
'[', ']', '{', '}','%','&','+','$','@','~','-','_','://','--','[]']) # remove it if you need punctuation
#remove stop words
%time texts = [[word for word in text if word not in stop_words] for text in tokenized_text]
Wall time: 25 ms
#create a Gensim dictionary from the texts
dictionary = corpora.Dictionary(texts)
#remove extremes (similar to the min/max df step used when creating the tf-idf matrix)
dictionary.filter_extremes(no_below=1, no_above=0.8)
#convert the dictionary to a bag of words corpus for reference
corpus = [dictionary.doc2bow(text) for text in texts]
%time lda = models.LdaModel(corpus, num_topics=5, id2word=dictionary, update_every=5, chunksize=10000, passes=100)
Wall time: 4min 32s
lda.show_topics()
[(0, '0.014*"system" + 0.013*"month" + 0.013*"use" + 0.011*"specifi" + 0.010*"gmail.com" + 0.009*"test" + 0.009*"data" + 0.009*"work" + 0.009*"applic" + 0.009*"manag"'), (1, '0.021*"work" + 0.018*"specifi" + 0.011*"gmail.com" + 0.011*"project" + 0.009*"engin" + 0.009*"materi" + 0.007*"draw" + 0.006*"system" + 0.006*"per" + 0.006*"site"'), (2, '0.012*"experi" + 0.010*"militari" + 0.010*"equip" + 0.009*"level" + 0.009*"oper" + 0.008*"like" + 0.008*"search" + 0.008*"help" + 0.007*"question" + 0.007*"https"'), (3, '0.011*"work" + 0.006*"financi" + 0.005*"project" + 0.005*"account" + 0.005*"skill" + 0.005*"gmail.com" + 0.005*"perform" + 0.004*"train" + 0.004*"oper" + 0.004*"specifi"'), (4, '0.014*"manag" + 0.012*"team" + 0.011*"train" + 0.011*"product" + 0.010*"custom" + 0.009*"sale" + 0.009*"busi" + 0.008*"process" + 0.008*"month" + 0.008*"work"')]
topics_matrix = lda.show_topics(formatted=False, num_words=20)
topics_matrix = np.array(topics_matrix,dtype=object)
topic_words = topics_matrix[:,1]
for i in topic_words:
print([str(word) for word in i])
print()
["('system', 0.013930067263968811)", "('month', 0.012975108381266243)", "('use', 0.012696189416809028)", "('specifi', 0.010734408675545164)", "('gmail.com', 0.0097738064518517551)", "('test', 0.0094430471895764635)", "('data', 0.0090699297840079497)", "('work', 0.0090291334880692464)", "('applic', 0.0087123249810280908)", "('manag', 0.0086790843163894668)", "('develop', 0.0085864569826078919)", "('support', 0.0084026275193507475)", "('project', 0.0083391632664919727)", "('network', 0.0081552516574210122)", '("\'s", 0.0074209337151694265)', "('server', 0.0070817186518829399)", "('user', 0.0069362805436867098)", "('team', 0.0061214123002106)", "('experi', 0.005985466848800935)", "('softwar', 0.0059212352661100328)"]
["('work', 0.020749515343922807)", "('specifi', 0.017795487007731838)", "('gmail.com', 0.01117747363893045)", "('project', 0.01052765394396204)", "('engin', 0.0091739740322179589)", "('materi', 0.0091465900454982627)", "('draw', 0.0074857332410223501)", "('system', 0.0060185492399684685)", "('per', 0.0058305082174600811)", "('site', 0.0056800336689002608)", "('report', 0.0054445895847909489)", "('u', 0.0050804782914841036)", "('respons', 0.0050381095440077215)", "('compani', 0.0048183000306104701)", "('procur', 0.0047220632092786468)", "('knowledg', 0.0045983400363514759)", "('offic', 0.0045441442899460185)", "('relat', 0.0044424991312514801)", "('organ', 0.0043137244542382361)", "('depart', 0.0042029447374624463)"]
["('experi', 0.012184798102428252)", "('militari', 0.010252787998611851)", "('equip', 0.0099335999976050953)", "('level', 0.0091669711248801838)", "('oper', 0.008950118965318268)", "('like', 0.0084846817988243634)", "('search', 0.0076955837567458437)", "('help', 0.0075414675779583665)", "('question', 0.0072223045519164376)", "('https', 0.0071684739750607837)", "('sign', 0.0071008138854451097)", "('se', 0.0070749404554425336)", "('candidateâ\\x80\\x99', 0.007074940442536524)", "('jowal', 0.0070749403001418741)", "('microsoft.com', 0.0070749402013923109)", "('able_to_start_in', 0.007074940200935396)", "('demo', 0.0070686067959676458)", "('educ', 0.0070575282693635241)", "('gmail.com', 0.0066903236114375349)", "('mainten', 0.0057050791904132859)"]
["('work', 0.010913718156158211)", "('financi', 0.0063066699386220351)", "('project', 0.0053101691596525178)", "('account', 0.0050999956066103223)", "('skill', 0.0050839839293918901)", "('gmail.com', 0.0048756259134724155)", "('perform', 0.004810146806259275)", "('train', 0.0044301904639987852)", "('oper', 0.0043973058220973535)", "('specifi', 0.0042313940588412959)", "('compani', 0.0042212832313983538)", "('per', 0.00420388827428633)", "('month', 0.0040447253335422137)", "('inventori', 0.0039844324167890004)", "('posit', 0.0039182962953043616)", "('client', 0.0038530551719803039)", "('day', 0.0035440357180713208)", "('cost', 0.0035437238033319786)", "('custom', 0.0034447838248956347)", "('would', 0.0034219684148195789)"]
["('manag', 0.013798482043245967)", "('team', 0.012082690697034272)", "('train', 0.01142888828557498)", "('product', 0.010616626347206821)", "('custom', 0.010219317425860231)", "('sale', 0.0093850973329967832)", "('busi', 0.009285692189428648)", "('process', 0.0081632891446117781)", "('month', 0.0077890355054027185)", "('work', 0.0075896736800478773)", "('client', 0.0073507448223083362)", "('develop', 0.006825933413822087)", "('market', 0.006633452119516518)", "('plan', 0.0063189307566972382)", "('new', 0.0063164800793325442)", "('report', 0.0061931891248566137)", "('various', 0.0059690050189707402)", "('employe', 0.0058563060582631455)", "('ensur', 0.0057431148931711079)", "('servic', 0.0057308517220293993)"]
import os
listdir = []
path = 'D:/xray production/resume backup/Docs_Backup/Resume_set_01/train'
listdir = [os.path.join(dirpath, f)
for dirpath, dirnames, files in os.walk(path)
for f in files if f.endswith('.txt')]
#not super pythonic, no, not at all.
#use extend so it's a big flat list of vocab
synopses = []
totalvocab_stemmed = []
totalvocab_tokenized = []
for fle in listdir:
# open the file and then call .read() to get the text
with open(fle, encoding="latin-1") as f:
text = f.read()
s = text
synopses.append(s)
for i in synopses:
allwords_stemmed = tokenize_and_stem(i) #for each item in 'synopses', tokenize/stem
totalvocab_stemmed.extend(allwords_stemmed) #extend the 'totalvocab_stemmed' list
allwords_tokenized = tokenize_only(i)
totalvocab_tokenized.extend(allwords_tokenized)
vocab_frame = pd.DataFrame({'words': totalvocab_tokenized}, index = totalvocab_stemmed)
print('there are ' + str(vocab_frame.shape[0]) + ' items in vocab_frame')
from sklearn.feature_extraction.text import TfidfVectorizer
#define vectorizer parameters
tfidf_vectorizer = TfidfVectorizer(max_df=100000, max_features=259405,
min_df=0, stop_words='english',
use_idf=True, tokenizer=tokenize_and_stem, ngram_range=(1,3))
%time tfidf_matrix = tfidf_vectorizer.fit_transform(synopses) #fit the vectorizer to synopses
print(tfidf_matrix.shape)
there are 251352 items in vocab_frame Wall time: 11.6 s (335, 259405)
tfidf_matrix
<335x259405 sparse matrix of type '<class 'numpy.float64'>' with 435010 stored elements in Compressed Sparse Row format>
km.predict(tfidf_matrix)
array([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3])
# iterate over the list getting each file
vocab = []
dictionary = []
dic_tf = {}
dic_df = {}
for fle in listdir:
# open the file and then call .read() to get the text
with open(fle, encoding="latin-1") as f:
text = f.read()
s = text
text = ' '.join(w for w in re.split(r"\W", s) if w)
text = wordpunct_tokenize(text)
for t in text:
if t not in stop_words:
if not t.isdigit():
t=t.lower()
if t.isdigit():
text.remove(t)
dictionary.append((text))
import gensim
dictio = gensim.corpora.Dictionary(dictionary)
corpus = [dictio.doc2bow(gen_doc) for gen_doc in dictionary]
tf_idf = gensim.models.TfidfModel(corpus)
sims = gensim.similarities.Similarity('D:/xray production/resume backup/Docs_Backup',tf_idf[corpus],
num_features=len(dictio))
print(sims)
print(type(sims))
Similarity index with 318 documents in 0 shards (stored under D:/xray production/resume backup/Docs_Backup) <class 'gensim.similarities.docsim.Similarity'>
# iterate over the list getting each file
text = []
listdir = []
path = 'D:/xray production/resume backup/Docs_Backup/Resume_set_01/test'
listdir = [os.path.join(dirpath, f)
for dirpath, dirnames, files in os.walk(path)
for f in files if f.endswith('.txt')]
for fle in listdir:
# open the file and then call .read() to get the text
with open(fle, encoding="latin-1") as f:
text.append(f.read())
query_doc = []
from nltk.tokenize import RegexpTokenizer
for i in range(len(text)):
s = text[i]
text[i] = ' '.join(w for w in re.split(r"\W", s) if w)
for t in text:
new_data = [i.lower() for i in wordpunct_tokenize(t) if i.lower() not in stop_words if not i.isdigit()]
for n in new_data:
if n.isdigit():
new_data.remove(n)
query_doc.append(new_data)
print(query_doc)
[['applicant', 'category', 'professional', 'requisition', 'code', 'two', 'initials', 'recruiter', 'lb', 'system', 'date', 'system', 'time', 'document', 'name', 'william', 'ward', 'ashmore', 'source', 'clearancejobs', 'com', 'resumes', 'searchterms', 'software', 'development', 'coding', 'test', 'researcher', 'build', 'user', 'interface', 'user', 'experience', 'url', 'http', 'www', 'clearancejobs', 'com', 'resumes', 'william', 'ward', 'ashmore', 'document', 'date', 'document', 'time', 'document', 'id', '42d62n82t', 'email', 'address', 'william', 'w', 'ashmore', 'gmail', 'com', 'location', 'al', 'start', 'resume', 'text', 'william', 'ward', 'ashmore', 'old', 'farm', 'lane', 'apt', 'e', 'prattville', 'alabama', 'date', 'added', 'contact', 'info', 'email', 'william', 'w', 'ashmore', 'gmail', 'com', 'mobile', 'security', 'clearance', 'clearance', 'status', 'active', 'security', 'clearance', 'clearance', 'level', 'secret', 'clearance', 'expiration', 'september', 'issuing', 'agency', 'dept', 'defense', 'polygraph', 'none', 'military', 'service', 'primary', 'branch', 'none', 'status', 'job', 'category', 'hardware', 'software', 'prog', 'career', 'level', 'entry', 'level', 'less', 'yrs', 'experience', 'type', 'passively', 'considering', 'jobs', 'geography', 'willing', 'relocate', 'yes', 'ideal', 'locations', 'al', 'birmingham', 'al', 'montgomery', 'al', 'northern', 'huntsville', 'compensation', 'desired', 'annual', 'unspecified', 'desired', 'hourly', 'unspecified', 'resume', 'body', 'william', 'ashmore', 'william', 'w', 'ashmore', 'gmail', 'com', 'old', 'farm', 'lane', 'apt', 'e', 'prattville', 'al', 'education', 'mississippi', 'state', 'university', 'starkville', 'ms', 'business', 'information', 'systems', 'graduated', 'may', 'holmes', 'community', 'college', 'grenada', 'campus', 'grenada', 'ms', 'associate', 'implied', 'science', 'graduated', 'may', 'profile', 'self', 'motivated', 'individual', 'yearning', 'willing', 'learn', 'much', 'looking', 'expand', 'career', 'career', 'field', 'currently', 'working', 'mcsa', 'mcse', 'certifications', 'certifications', 'security', 'november', 'present', 'comptia', 'security', 'edition', 'certification', 'exam', 'secret', 'security', 'clearance', 'september', 'september', 'ashmore', 'william', 'w', 'knowledge', 'skills', 'â', 'comptia', 'security', 'â', 'windows', 'operating', 'systems', 'â', 'ms', 'office', 'word', 'excel', 'outlook', 'access', 'powerpoint', 'â', 'internet', 'explorer', 'mozilla', 'browsers', 'â', 'bmc', 'remedy', 'ticket', 'manager', 'system', 'â', 'installation', 'cat', 'fiber', 'cables', 'â', 'active', 'directory', 'administration', 'â', 'database', 'management', 'â', 'air', 'force', 'network', 'operations', 'â', 'troubleshooting', 'desktops', 'laptops', 'printers', 'scanners', 'pdas', 'projectors', 'â', 'digital', 'certificates', 'â', 'dreamweaver', 'suite', 'â', 'anti', 'virus', 'tools', 'â', 'knowledge', 'c', 'html', 'java', 'script', 'cobol', 'visual', 'basic', 'rpg', 'oracle', 'sql', 'â', 'remote', 'administration', 'remote', 'assistance', 'â', 'adding', 'workstations', 'domain', 'â', 'access', 'control', 'methods', 'â', 'encryption', 'standards', 'â', 'network', 'device', 'mapping', 'â', 'microsoft', 'exchange', 'servers', 'â', 'administrative', 'assistant', 'â', 'installation', 'tele', 'conference', 'systems', 'â', 'commissioned', 'decommissioned', 'servers', 'â', 'niprnet', 'siprnet', 'systems', 'experience', 'network', 'support', 'specialist', 'integrated', 'computer', 'solutions', 'inc', 'montgomery', 'al', 'september', 'present', 'disa', 'september', 'â', 'present', 'gunter', 'air', 'force', 'base', 'experience', 'duties', 'â', 'install', 'fiber', 'cat', 'cat', 'cables', 'throughout', 'building', 'â', 'help', 'ensure', 'server', 'roomâ', 'run', 'without', 'connectivity', 'loss', 'â', 'troubleshoot', 'various', 'connectivity', 'issues', 'users', 'building', 'â', 'install', 'troubleshoot', 'phone', 'systems', 'voip', 'systems', 'â', 'terminate', 'cat', 'fiber', 'cables', 'needed', 'â', 'coordinate', 'user', 'moves', 'throughout', 'building', 'ensure', 'network', 'loss', 'minimum', 'â', 'coordinate', 'central', 'communications', 'center', 'ccc', 'port', 'security', 'issues', 'positions', 'promotions', 'â', 'network', 'support', 'specialist', 'â', 'september', 'network', 'support', 'technician', 'indus', 'corp', 'montgomery', 'al', 'june', 'september', 'enterprise', 'service', 'desk', 'june', 'â', 'present', 'gunter', 'air', 'force', 'base', 'experience', 'duties', 'â', 'provide', 'telephone', 'technical', 'support', 'â', 'network', 'administration', 'remedy', 'administration', 'â', 'problem', 'resolution', 'advanced', 'troubleshoot', 'support', 'â', 'provide', 'remote', 'assistance', 'remote', 'administration', 'â', 'train', 'employees', 'steps', 'troubleshoot', 'issues', 'positions', 'promotions', 'â', 'level', 'helpdesk', 'network', 'support', 'technician', 'â', 'june', 'â', 'level', 'ii', 'helpdesk', 'network', 'support', 'technician', 'â', 'november', 'â', 'advanced', 'level', 'helpdesk', 'network', 'support', 'technician', 'â', 'september', 'â', 'promoted', 'remote', 'administration', 'pit', 'december', 'â', 'promoted', 'area', 'migration', 'pit', 'may', 'co', 'op', 'worker', 'american', 'cast', 'iron', 'pipe', 'company', 'acipco', 'birmingham', 'al', 'january', 'may', 'august', 'â', 'december', 'may', 'july', 'experience', 'duties', 'â', 'perform', 'maintenance', 'printers', 'computers', 'various', 'devices', 'â', 'updated', 'sql', 'programs', 'improve', 'productivity', 'â', 'updated', 'access', 'database', 'include', 'new', 'variables', 'â', 'perform', 'general', 'office', 'duties', 'positions', 'â', 'co', 'op', 'worker', 'student', 'technician', 'specialist', 'high', 'performance', 'computing', 'collaboratory', 'â', 'starkville', 'ms', 'august', 'â', 'december', 'may', 'â', 'july', 'january', 'â', 'may', 'august', 'â', 'may', 'experience', 'duties', 'â', 'perform', 'maintenance', 'printers', 'computers', 'various', 'devices', 'repairs', 'throughout', 'building', 'â', 'set', 'tele', 'conference', 'systems', 'â', 'install', 'cat', 'cat', 'fiber', 'cables', 'building', 'â', 'answer', 'user', 'inquiries', 'regarding', 'computer', 'software', 'positions', 'â', 'student', 'technician', 'specialist', 'â', 'august', 'additional', 'information', 'honors', 'activities', 'level', 'ii', 'employee', 'month', 'highest', 'amount', 'fixed', 'tickets', 'december', 'young', 'alumni', 'representative', 'mississippi', 'state', 'alumni', 'association', 'montgomery', 'area', 'resume', 'flag', 'add', 'network', 'end', 'resume', 'text'], ['applicant', 'category', 'professional', 'requisition', 'code', 'two', 'initials', 'recruiter', 'lb', 'system', 'date', 'system', 'time', 'document', 'name', 'jerry', 'bolanos', 'source', 'careerbuilder', 'resumes', 'wsi', 'recent', 'searchterms', 'software', 'development', 'coding', 'test', 'researcher', 'build', 'user', 'interface', 'user', 'experience', 'url', 'https', 'ws', 'careerbuilder', 'com', 'resumes', 'resumes', 'asmx', 'v2_getresume', 'packet', 'packet', 'document', 'date', 'document', 'time', 'document', 'id', '42i42n7yy', 'email', 'address', 'jerrybolanos', 'hotmail', 'com', 'location', 'ca', 'start', 'resume', 'text', 'jerry', 'bolanos', 'workman', 'mill', 'road', 'unit', 'whittier', 'ca', 'cell', 'email', 'jerrybolanos', 'hotmail', 'com', 'professional', 'experience', 'beamonstar', 'products', 'inc', 'mfg', 'novelty', 'toys', 'enhancements', 'queen', 'creek', 'az', 'may', 'august', 'operations', 'accounting', 'manager', 'responsible', 'financial', 'activities', 'performed', 'day', 'day', 'accounting', 'functions', 'manage', 'daily', 'cash', 'flow', 'maintain', 'general', 'ledger', 'system', 'banking', 'credit', 'card', 'transactions', 'online', 'website', 'payments', 'issuance', 'review', 'check', 'credit', 'card', 'expenditures', 'oversee', 'account', 'receivables', 'review', 'r', 'aging', 'summary', 'followed', 'customers', 'collections', 'overdue', 'payments', 'cost', 'accounting', 'standard', 'cost', 'accounting', 'developed', 'maintained', 'bill', 'materials', 'costing', 'finished', 'good', 'products', 'review', 'accuracy', 'costs', 'inventory', 'perform', 'inventory', 'valuation', 'variance', 'analysis', 'explanations', 'analysis', 'freight', 'shipping', 'costs', 'inventory', 'control', 'perform', 'monthly', 'physical', 'inventory', 'analysis', 'inventory', 'discrepancies', 'prepared', 'inventory', 'turnover', 'reports', 'inventory', 'aging', 'reports', 'identified', 'slow', 'moving', 'obsolete', 'items', 'min', 'max', 'levels', 'inventory', 'procurement', 'purchasing', 'sourcing', 'contract', 'manufacturers', 'private', 'labeling', 'within', 'pharmaceutical', 'industry', 'sourcing', 'printing', 'packaging', 'suppliers', 'fulfillment', 'centers', 'trucking', 'freight', 'carriers', 'along', 'purchasing', 'supplies', 'needed', 'temple', 'inland', 'inc', 'paperboard', 'box', 'manufacturing', 'bell', 'ca', 'may', 'april', 'accounting', 'office', 'manager', 'performed', 'month', 'end', 'activities', 'especially', 'related', 'inventory', 'supervision', 'weekly', 'payroll', 'processing', 'union', 'employees', 'supervise', 'accounts', 'receivables', 'collection', 'receivables', 'receipt', 'incoming', 'goods', 'services', 'via', 'purchase', 'orders', 'jd', 'edwards', 'system', 'cost', 'accounting', 'performed', 'inventory', 'valuation', 'variance', 'analysis', 'labor', 'utilization', 'reports', 'reviewed', 'inventory', 'costs', 'accuracy', 'created', 'maintained', 'updated', 'bill', 'materials', 'costing', 'products', 'set', 'procedures', 'adjust', 'accounting', 'policies', 'mandated', 'corporate', 'office', 'physical', 'inventory', 'overseen', 'weekly', 'cycle', 'counts', 'performed', 'monthly', 'physical', 'inventories', 'raw', 'material', 'roll', 'stock', 'finished', 'goods', 'variance', 'analysis', 'major', 'inventory', 'discrepancies', 'analyzed', 'variances', 'made', 'adjustments', 'inventory', 'needed', 'reporting', 'capital', 'expenditures', 'fixed', 'assets', 'key', 'performance', 'indicators', 'ran', 'customized', 'jd', 'edwards', 'reports', 'sales', 'department', 'coordinated', 'audits', 'necessary', 'l', 'shower', 'door', 'enclosures', 'manufacturing', 'ontario', 'ca', 'december', 'march', 'accounting', 'consultant', 'contractor', 'performed', 'month', 'end', 'closing', 'process', 'bank', 'account', 'reconciliations', 'prepared', 'year', 'end', 'audit', 'lead', 'schedules', 'supporting', 'schedules', 'labor', 'payroll', 'reporting', 'cost', 'accounting', 'prepared', 'variance', 'analysis', 'materials', 'usage', 'purchase', 'price', 'variances', 'assisted', 'expediting', 'monthly', 'reporting', 'procedures', 'accretive', 'solutions', 'accounting', 'finance', 'consulting', 'irvine', 'ca', 'july', 'october', 'business', 'consultant', 'contractor', 'unitedhealth', 'group', 'formerly', 'known', 'pacificare', 'cypress', 'ca', 'authentic', 'specialty', 'foods', 'la', 'victoria', 'embasa', 'food', 'manufacturing', 'chino', 'ca', 'october', 'may', 'director', 'production', 'planning', 'quality', 'systems', 'rosemead', 'plant', 'june', 'may', 'manager', 'cost', 'accounting', 'plant', 'controller', 'rosemead', 'plant', 'october', 'may', 'production', 'planning', 'preparation', 'annual', 'weekly', 'daily', 'production', 'plan', 'plant', 'six', 'production', 'lines', 'hispanic', 'food', 'products', 'salsas', 'sauces', 'fresh', 'produce', 'experience', 'understanding', 'material', 'availability', 'production', 'line', 'constraints', 'downtime', 'produce', 'seasonality', 'packaging', 'solid', 'understanding', 'various', 'types', 'packaging', 'glass', 'bottles', 'jars', 'plastic', 'jugs', 'bottles', 'metal', 'cans', 'flexible', 'cups', 'portion', 'control', 'pouches', 'pc', 'cost', 'accounting', 'sound', 'understanding', 'standard', 'cost', 'system', 'month', 'end', 'close', 'preparing', 'financial', 'statements', 'performing', 'variance', 'analysis', 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len(query_doc)
335
query_doc_bow = []
for doc in query_doc:
query_doc_bow.append(dictio.doc2bow(doc))
len(query_doc_bow)
335
query_doc_tf_idf = []
for doc in query_doc_bow:
query_doc_tf_idf.append(tf_idf[query_doc_bow])
len(query_doc_tf_idf)
335
query_doc_bow
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1), (9560, 2), (9707, 1), (9763, 2), (10219, 1), (10386, 2), (10942, 1), (11158, 1), (11222, 1), (11450, 2), (11614, 2), (11644, 1), (11791, 2), (11820, 1), (12074, 1), (12121, 2), (12257, 7), (12544, 1), (12760, 10), (12762, 6), (12765, 1), (12766, 2), (12767, 2), (12768, 2), (12769, 1), (12770, 1), (12771, 1), (12776, 1), (12780, 1), (12788, 1), (12789, 3), (12792, 1), (12801, 1), (12816, 1), (12818, 1), (12820, 1), (12823, 1), (12832, 2), (12837, 1), (12839, 1), (12843, 1), (12845, 1), (12849, 1), (12852, 1), (12854, 1), (12855, 1), (12856, 1), (12868, 1), (12871, 3), (12872, 1), (12873, 2), (12875, 2), (12877, 2), (12880, 1), (12882, 3), (12883, 1), (12885, 1), (12887, 1), (12892, 2), (12893, 1), (12894, 1), (12895, 1), (12905, 1), (12992, 1), (13084, 5), (13500, 1), (13534, 1), (13724, 2), (13778, 1), (13831, 1), (14272, 1), (14434, 2), (14793, 1), (15316, 1), (16373, 1), (16708, 1), (17677, 2), (18972, 8), (19894, 1), (20634, 1), (21346, 1), (22087, 1), (22863, 2)], [(22, 6), (25, 6), (30, 3), (31, 1), (33, 5), (35, 2), (36, 1), (38, 1), (49, 4), (147, 1), (166, 1), (170, 1), (181, 3), (256, 1), (280, 1), (281, 6), (292, 1), (296, 1), (305, 14), (312, 12), (324, 1), (325, 2), (328, 1), (354, 1), (356, 7), (360, 1), (364, 2), (365, 2), (370, 1), (382, 5), (445, 2), (449, 1), (451, 8), (456, 1), (468, 1), (477, 1), (488, 1), (491, 2), (519, 2), (542, 2), (558, 1), (595, 1), (600, 2), (619, 1), (621, 1), (662, 1), (694, 1), (702, 1), (730, 1), (747, 3), (775, 6), (778, 1), (790, 2), (806, 2), (810, 4), (836, 2), (846, 1), (849, 1), (851, 2), (855, 1), (876, 1), (878, 1), (880, 3), (881, 1), (894, 1), (895, 1), (898, 4), (912, 2), (944, 1), (963, 2), (964, 3), (971, 2), (987, 4), (988, 2), (1000, 1), (1031, 2), (1039, 2), (1054, 1), (1058, 2), (1060, 2), (1061, 1), (1062, 2), (1155, 2), (1156, 2), (1162, 8), (1163, 1), (1164, 1), (1183, 6), (1184, 6), (1187, 1), (1207, 1), (1208, 2), (1209, 5), (1213, 1), (1220, 1), (1221, 1), (1224, 6), (1231, 1), (1250, 1), 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(810, 3), (836, 1), (849, 1), (876, 1), (880, 2), (944, 1), (963, 1), (964, 2), (987, 1), (988, 1), (1007, 1), (1052, 1), (1060, 1), (1164, 1), (1176, 1), (1227, 1), (1230, 1), (1286, 3), (1331, 1), (1333, 1), (1350, 1), (1352, 1), (1386, 2), (1458, 3), (1512, 1), (1522, 1), (1536, 1), (1538, 1), (1563, 1), (1599, 2), (1671, 2), (1673, 1), (1680, 1), (1773, 1), (1774, 3), (1805, 1), (1893, 1), (1924, 4), (1959, 4), (1988, 2), (2044, 1), (2046, 1), (2056, 2), (2062, 1), (2082, 1), (2127, 1), (2200, 2), (2234, 2), (2256, 1), (2424, 1), (2457, 1), (2501, 1), (2610, 1), (2679, 2), (2744, 1), (2787, 2), (2808, 1), (2969, 1), (3019, 1), (3075, 1), (3085, 5), (3116, 1), (3276, 4), (3313, 1), (3434, 1), (3655, 1), (3834, 1), (3927, 1), (3981, 3), (3995, 2), (4055, 1), (4473, 1), (4612, 6), (4700, 1), (4919, 3), (5084, 1), (5151, 1), (5200, 1), (5548, 1), (5552, 2), (5677, 1), (5753, 1), (5754, 1), (5755, 1), (5756, 2), (5757, 1), (5777, 1), (5778, 1), (5844, 2), (6462, 1), (6550, 2), (6560, 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(20598, 2), (20806, 3), (21066, 1), (21086, 1), (21109, 1), (21421, 1), (21944, 1), (21947, 2), (22539, 1), (22556, 6), (22557, 3), (22970, 1), (23395, 3), (23627, 1)], [(22, 6), (28, 6), (29, 1), (30, 2), (31, 2), (32, 2), (33, 1), (38, 3), (49, 2), (148, 1), (295, 5), (296, 4), (302, 1), (354, 4), (355, 1), (357, 1), (360, 2), (366, 2), (368, 1), (370, 2), (376, 2), (382, 2), (404, 2), (405, 1), (406, 1), (407, 2), (408, 1), (441, 3), (444, 1), (448, 1), (449, 3), (451, 2), (470, 1), (488, 1), (497, 1), (505, 1), (508, 2), (516, 1), (522, 1), (523, 2), (532, 2), (542, 1), (543, 1), (560, 2), (570, 1), (595, 2), (600, 1), (663, 2), (682, 1), (698, 3), (731, 1), (738, 4), (759, 2), (783, 1), (790, 1), (792, 1), (799, 1), (806, 2), (816, 1), (846, 1), (849, 1), (859, 1), (870, 1), (950, 1), (963, 2), (965, 1), (968, 1), (987, 1), (988, 4), (1007, 2), (1024, 2), (1176, 1), (1184, 2), (1187, 4), (1207, 5), (1210, 1), (1224, 1), (1227, 1), (1230, 1), (1286, 1), (1307, 1), (1329, 1), (1351, 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1), (3693, 2), (3715, 1), (3719, 1), (3812, 1), (3927, 1), (3981, 1), (4031, 1), (4127, 2), (4265, 1), (4303, 1), (4612, 6), (4706, 4), (4746, 1), (4767, 3), (4966, 1), (5084, 1), (5230, 1), (5572, 1), (6165, 1), (6180, 1), (6224, 1), (6550, 1), (6612, 2), (6696, 1), (6811, 1), (7000, 1), (7210, 3), (7393, 1), (7406, 1), (7540, 1), (7660, 1), (7950, 1), (7956, 1), (8045, 1), (8189, 1), (8903, 1), (8956, 1), (9011, 3), (9158, 4), (9454, 1), (9540, 1), (9839, 4), (9882, 1), (9912, 2), (10343, 1), (10367, 1), (10386, 2), (10711, 1), (10942, 1), (10961, 1), (11127, 1), (11644, 1), (12000, 2), (12257, 2), (12988, 1), (13487, 2), (15359, 2), (16811, 1), (18750, 1), (18879, 1), (20054, 1), (20746, 1), (20747, 1), (21990, 1)]]
similarity = []
for doc in query_doc_tf_idf:
similarity.append(sims[doc])
len(similarity)
335
similarity[0][0]
array([ 0.35054645, 0.02322643, 0.08233488, 0.02395826, 0.00619038,
0.00314484, 0.00269095, 0.03492863, 0.01003387, 0.0245425 ,
0.02250338, 0.00713318, 0.00294751, 0.02309033, 0.0053916 ,
0.00553387, 0.00711232, 0.00329042, 0.01363345, 0.00272545,
0.02532524, 0.0557736 , 0.0286211 , 0.08015185, 0.02185713,
0.01369142, 0.0042667 , 0.00727741, 0.00391798, 0.00273466,
0.00740061, 0.00467924, 0.01406436, 0.0072231 , 0.02634818,
0.01153965, 0.00308613, 0.02368234, 0.01297817, 0.01842993,
0.03860107, 0.00693154, 0.0274331 , 0.01729154, 0.03050437,
0.01313151, 0.01739317, 0.00521036, 0.00947314, 0.00218326,
0.00658219, 0.00834381, 0.02005658, 0.0217478 , 0.01233016,
0.02771564, 0.01719856, 0.10294344, 0.09369256, 0.02425669,
0.08298051, 0.03256612, 0.01070248, 0.01108552, 0.02753989,
0.10012275, 0.05605416, 0.11510827, 0.0232465 , 0.09911746,
0.01461298, 0.01417352, 0.0056964 , 0.12498124, 0.0953934 ,
0.00556504, 0.02269531, 0.01565271, 0.01858254, 0.04299607,
0.01662035, 0.02162583, 0.04532538, 0.01643004, 0.02703335,
0.08605653, 0.02081544, 0.01519476, 0.05317049, 0.03198909,
0.01848956, 0.08331764, 0.01453926, 0.01825512, 0.01399072,
0.01802641, 0.02826901, 0.02800825, 0.05036802, 0.02612917,
0.02366781, 0.01468317, 0.00145764, 0.02427195, 0.0127825 ,
0.03987787, 0.01766597, 0.00592704, 0.09271538, 0.00726364,
0.06445262, 0.02741383, 0.0322242 , 0.02341901, 0.03867365,
0.0188643 , 0.01058226, 0.00205401, 0.01467214, 0.02713296,
0.04246601, 0.04317128, 0.03385909, 0.01435984, 0.02042105,
0.02225098, 0.02174482, 0.02339743, 0.06111853, 0.04136331,
0.0138119 , 0.0852966 , 0.00572752, 0.05203102, 0.03078152,
0.02001892, 0.0520145 , 0.03085907, 0.0143176 , 0.02044833,
0.01508812, 0.01816299, 0.00460326, 0.0269683 , 0.02144251,
0.01300844, 0.00994996, 0.01451043, 0.02937867, 0.01723943,
0.02348622, 0.04452385, 0.01099504, 0.00220673, 0.10101219,
0.00250933, 0.01725961, 0.02255923, 0.01715993, 0.01883953,
0.0573232 , 0.0132557 , 0.02013709, 0.0104466 , 0.01683415,
0.01635586, 0.03366829, 0.02541452, 0.06366467, 0.06625686,
0.00720933, 0.00750504, 0.00958594, 0.06095486, 0.01134488,
0.00952384, 0.01134127, 0.00695598, 0.05977414, 0.10160333,
0.02339192, 0.03211993, 0.02105389, 0.04631499, 0.04698536,
0.02601309, 0.01012518, 0.01221919, 0.02251235, 0.08807086,
0.06246712, 0.00656146, 0.03011168, 0.0024086 , 0.00035623,
0.00337876, 0.01568099, 0.0574212 , 0.00748183, 0.00442449,
0.01707185, 0.01061932, 0.03426392, 0.01105251, 0.01094768,
0.00576013, 0.08412689, 0.04420948, 0.04935362, 0.00260568,
0.01118236, 0.04770581, 0.01766897, 0.01844238, 0.01540919,
0.00785517, 0.01394981, 0.01407619, 0.00689036, 0.01475567,
0.00723241, 0.00596206, 0.02884636, 0.02865647, 0.16466284,
0.015904 , 0.00111235, 0.01508474, 0.03096221, 0.00463283,
0.09825269, 0.01231819, 0.00182881, 0.0110896 , 0.00916402,
0.01928507, 0.01645496, 0.01009873, 0.02612328, 0.02373157,
0.01377286, 0.03957132, 0.05395267, 0.00996127, 0.01828298,
0.0739735 , 0.01028945, 0.03196759, 0.02445733, 0.0373247 ,
0.08051181, 0.01751152, 0.02983894, 0.0100641 , 0.00216016,
0.01232081, 0.00265924, 0.00930898, 0.0108438 , 0.01717886,
0.06725563, 0.02182888, 0.01231385, 0.01660265, 0.01376816,
0.00608671, 0.02116447, 0.00925395, 0.0124741 , 0.04149848,
0.00682273, 0.00845872, 0.0190417 , 0.01014022, 0.00993866,
0.03229592, 0.00839318, 0.01332373, 0.04939602, 0.02153205,
0.09995067, 0.01291488, 0.01658006, 0.01979341, 0.08683468,
0.01978173, 0.02657486, 0.00057747, 0.02087271, 0.14429076,
0.01740403, 0.0215031 , 0.01196509, 0.03495681, 0.10711339,
0.02601366, 0.03753863, 0.00847 , 0.020243 , 0.01029386,
0.05940956, 0.01909327, 0.0134836 , 0.01593511, 0.10836479,
0.0849105 , 0.07868181, 0.01406886, 0.02038061, 0.03342033,
0.01555219, 0.01528482, 0.00896989, 0.06215353, 0.02570961,
0.026081 , 0.00672384, 0.00652127], dtype=float32)